Import Libraries¶

In [1]:
import pandas as pd
import numpy as np
import plotly.express as py
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
import matplotlib.pyplot as plt
In [2]:
# Load the Titanic dataset
df = pd.read_csv('train.csv')
In [3]:
# Diasplay the loaded dataset
df
Out[3]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

891 rows × 12 columns

Explore the Dataset¶

In [4]:
df.shape
Out[4]:
(891, 12)
In [5]:
df.columns
Out[5]:
Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')
In [6]:
df.head(2)
Out[6]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
In [7]:
df.tail(2)
Out[7]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.00 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.75 NaN Q
In [8]:
# Data Types and Missing Values 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
In [9]:
# Summary Statistics
df.describe()
Out[9]:
PassengerId Survived Pclass Age SibSp Parch Fare
count 891.000000 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208
std 257.353842 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429
min 1.000000 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000
25% 223.500000 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400
50% 446.000000 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200
75% 668.500000 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000
max 891.000000 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200

Data Cleaning¶

In [10]:
# Check the missing values
df.isnull().sum()
Out[10]:
PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64
In [11]:
# Fill the age column with the mean 
df['Age'].fillna(df['Age'].mean(), inplace = True)
In [12]:
# fill the Embarked column with the Mode
df['Embarked'].fillna(df['Embarked'].mode()[0], inplace = True)
In [13]:
# drop the Unnecessary
df.drop(['PassengerId','Name','Ticket', 'Cabin'], axis= 1, inplace = True)
In [14]:
#Feature Engineering
# Create a new feature 'FamilySize' by combining 'SibSp' and 'Parch'
df['FamilySize'] = df['SibSp'] + df['Parch'] + 1

# Create a new feature 'Singles'
df['Singles'] = np.where(df['FamilySize'] == 1,  1, 0)
In [15]:
df
Out[15]:
Survived Pclass Sex Age SibSp Parch Fare Embarked FamilySize Singles
0 0 3 male 22.000000 1 0 7.2500 S 2 0
1 1 1 female 38.000000 1 0 71.2833 C 2 0
2 1 3 female 26.000000 0 0 7.9250 S 1 1
3 1 1 female 35.000000 1 0 53.1000 S 2 0
4 0 3 male 35.000000 0 0 8.0500 S 1 1
... ... ... ... ... ... ... ... ... ... ...
886 0 2 male 27.000000 0 0 13.0000 S 1 1
887 1 1 female 19.000000 0 0 30.0000 S 1 1
888 0 3 female 29.699118 1 2 23.4500 S 4 0
889 1 1 male 26.000000 0 0 30.0000 C 1 1
890 0 3 male 32.000000 0 0 7.7500 Q 1 1

891 rows × 10 columns

Exploratory Data Analysis(EDA)¶

In [16]:
fig = py.scatter(df, x='Fare', y='Age', color='Survived', size='Fare')
fig.show()
In [17]:
# Passenger Survival

fig = py.pie(df, names='Survived',title='Passenger Survival',hole=0.4)
fig.show()
In [18]:
# Distribution of age 

fig = py.histogram(df, x='Age', nbins=30, marginal='box')
fig.show()
In [19]:
# Survival rate by Age and Pclass

fig = py.box(df,x='Pclass',y='Age')
fig.show()
In [20]:
# Create a heatmap

fig = py.density_heatmap(df, x='Embarked', y="Pclass", height=500, width=500)
fig.show()
In [21]:
# Correlation Among Training Features

fig = py.imshow(df.corr(method='pearson'),title='Correlation Among Training Features',height=700, width=700)
fig.show()
In [22]:
# Create a Wordcloud

text = " ".join(df.columns)
stopwords = set(STOPWORDS)
wordcloud = WordCloud(stopwords=stopwords, background_color="white").generate(text)
plt.figure( figsize=(15,10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

Conclusion¶

Let's summarize the relationships, patterns, and trends identified from the exploratory data analysis (EDA) of the Titanic dataset:

1 Survival Rate by Sex and Pclass:

The survival rate for females is significantly higher than for males across all passenger classes (Pclass). Females had a higher chance of survival. The survival rate decreases as the passenger class (Pclass) increases. Passengers in higher classes (1st class) had a better chance of survival compared to lower classes (2nd and 3rd class)

2 Survival Rate by Age, Sex, and Pclass:

Generally, children had a higher survival rate compared to adults, especially in the 2nd and 3rd classes.

3 Survival Rate by Age and Embarked:

The age distribution of survivors and non-survivors is similar across the different embarkation points (C = Cherbourg, Q = Queenstown, S = Southampton). There is no significant pattern suggesting a strong relationship between the port of embarkation and survival rate based on age.

4 Survival Rate by Fare and Pclass:

Passengers who paid a higher fare (indicating higher class) had a better survival rate within each passenger class. For each class, those who paid more (likely indicating a better cabin or location) had a slightly higher chance of survival. Overall, being female and belonging to a higher passenger class were associated with higher survival rates

In [ ]: